- [[⚛️Learning continuous molecular descriptors by translating equivalent chemical representations]] - Axiomatic Attribution for Deep Networks is a powerful method for understanding and interpreting neural networks. It uses an attribution function to determine the importance of input features in network decisions. The Integrated Gradient method, a key technique in this approach, offers a non-intrusive way to analyze networks with minimal computational overhead. This method is particularly useful in fields like medical diagnostics and rule extraction. By adhering to fundamental axioms such as sensitivity and linearity, Axiomatic Attribution provides valuable insights into network behavior and decision-making processes. - [[🖼️Axiomatic Attribution for Deep Networks]] - Enhancing molecular descriptors for precise understanding: A novel approach using neural network learning combines simplified representations and machine translation techniques to improve molecular property predictions. By incorporating property classifiers and translating between chemical notations, this method creates efficient molecular representations. The approach significantly enhances performance in regression and classification tasks, opening new avenues for more accurate and coherent molecular analysis in chemistry and molecular biology. This advancement represents a crucial step in our ability to represent and analyze complex molecular structures, potentially impacting various scientific fields.